Series approximations for Rayleigh distributions of arbitrary dimensions and covariance matricesCitation formats

  • Authors:
  • Martin Wiegand
  • Saraleesan Nadarajah

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Series approximations for Rayleigh distributions of arbitrary dimensions and covariance matrices. / Wiegand, Martin; Nadarajah, Saraleesan.

In: Signal Processing, Vol. 165, 12.2019, p. 20-29.

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Wiegand, Martin ; Nadarajah, Saraleesan. / Series approximations for Rayleigh distributions of arbitrary dimensions and covariance matrices. In: Signal Processing. 2019 ; Vol. 165. pp. 20-29.

Bibtex

@article{55b74c0a32644b00a93efe3ca27a56f9,
title = "Series approximations for Rayleigh distributions of arbitrary dimensions and covariance matrices",
abstract = "The multivariate Rayleigh distribution is of crucial importance to many applied problems of engineering, such as in the analysis of multi-antenna wireless systems. Due to the lack of a generalised closed form of the distribution, the dependence on effective approximation methods for evaluation has created numerous numerical approaches with considerable restrictions in both dimensionality, as well as the structure of covariance matrices. In this paper we extend a previously introduced method [1] without either of these limitations. We then compare the performance of the new algorithms to recent integration methods of fixed dimension, presented by Beaulie and Zhang [2] and highlight the advantages of the new method.",
keywords = "Correlation, Covariance, Signal Processing, Multivariaterayleigh distribution",
author = "Martin Wiegand and Saraleesan Nadarajah",
year = "2019",
month = dec,
doi = "10.1016/j.sigpro.2019.06.035",
language = "English",
volume = "165",
pages = "20--29",
journal = "Signal Processing",
issn = "0165-1684",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Series approximations for Rayleigh distributions of arbitrary dimensions and covariance matrices

AU - Wiegand, Martin

AU - Nadarajah, Saraleesan

PY - 2019/12

Y1 - 2019/12

N2 - The multivariate Rayleigh distribution is of crucial importance to many applied problems of engineering, such as in the analysis of multi-antenna wireless systems. Due to the lack of a generalised closed form of the distribution, the dependence on effective approximation methods for evaluation has created numerous numerical approaches with considerable restrictions in both dimensionality, as well as the structure of covariance matrices. In this paper we extend a previously introduced method [1] without either of these limitations. We then compare the performance of the new algorithms to recent integration methods of fixed dimension, presented by Beaulie and Zhang [2] and highlight the advantages of the new method.

AB - The multivariate Rayleigh distribution is of crucial importance to many applied problems of engineering, such as in the analysis of multi-antenna wireless systems. Due to the lack of a generalised closed form of the distribution, the dependence on effective approximation methods for evaluation has created numerous numerical approaches with considerable restrictions in both dimensionality, as well as the structure of covariance matrices. In this paper we extend a previously introduced method [1] without either of these limitations. We then compare the performance of the new algorithms to recent integration methods of fixed dimension, presented by Beaulie and Zhang [2] and highlight the advantages of the new method.

KW - Correlation

KW - Covariance

KW - Signal Processing

KW - Multivariaterayleigh distribution

U2 - 10.1016/j.sigpro.2019.06.035

DO - 10.1016/j.sigpro.2019.06.035

M3 - Article

VL - 165

SP - 20

EP - 29

JO - Signal Processing

JF - Signal Processing

SN - 0165-1684

ER -